102,708 research outputs found
Gibbs sampling with people
A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications
Gibbs sampling with people
A core problem in cognitive science and machine learning is to understand how
humans derive semantic representations from perceptual objects, such as color
from an apple, pleasantness from a musical chord, or seriousness from a face.
Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying
such representations, in which participants are presented with binary choice
trials constructed such that the decisions follow a Markov Chain Monte Carlo
acceptance rule. However, while MCMCP has strong asymptotic properties, its
binary choice paradigm generates relatively little information per trial, and
its local proposal function makes it slow to explore the parameter space and
find the modes of the distribution. Here we therefore generalize MCMCP to a
continuous-sampling paradigm, where in each iteration the participant uses a
slider to continuously manipulate a single stimulus dimension to optimize a
given criterion such as 'pleasantness'. We formulate both methods from a
utility-theory perspective, and show that the new method can be interpreted as
'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation
parameter to the transition step, and show that this parameter can be
manipulated to flexibly shift between Gibbs sampling and deterministic
optimization. In an initial study, we show GSP clearly outperforming MCMCP; we
then show that GSP provides novel and interpretable results in three other
domains, namely musical chords, vocal emotions, and faces. We validate these
results through large-scale perceptual rating experiments. The final
experiments use GSP to navigate the latent space of a state-of-the-art image
synthesis network (StyleGAN), a promising approach for applying GSP to
high-dimensional perceptual spaces. We conclude by discussing future cognitive
applications and ethical implications
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different
materials, which correlates with human similarity judgments. We first create a
database of 9,000 rendered images depicting objects with varying materials,
shape and illumination. We then gather data on perceived similarity from
crowdsourced experiments; our analysis of over 114,840 answers suggests that
indeed a shared perception of appearance similarity exists. We feed this data
to a deep learning architecture with a novel loss function, which learns a
feature space for materials that correlates with such perceived appearance
similarity. Our evaluation shows that our model outperforms existing metrics.
Last, we demonstrate several applications enabled by our metric, including
appearance-based search for material suggestions, database visualization,
clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure
The constitution of visual perceptual units in the functional architecture of V1
Scope of this paper is to consider a mean field neural model which takes into
account the functional neurogeometry of the visual cortex modelled as a group
of rotations and translations. The model generalizes well known results of
Bressloff and Cowan which, in absence of input, accounts for hallucination
patterns. The main result of our study consists in showing that in presence of
a visual input, the eigenmodes of the linearized operator which become stable
represent perceptual units present in the image. The result is strictly related
to dimensionality reduction and clustering problems
Frame Theory for Signal Processing in Psychoacoustics
This review chapter aims to strengthen the link between frame theory and
signal processing tasks in psychoacoustics. On the one side, the basic concepts
of frame theory are presented and some proofs are provided to explain those
concepts in some detail. The goal is to reveal to hearing scientists how this
mathematical theory could be relevant for their research. In particular, we
focus on frame theory in a filter bank approach, which is probably the most
relevant view-point for audio signal processing. On the other side, basic
psychoacoustic concepts are presented to stimulate mathematicians to apply
their knowledge in this field
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